Universal Pattern Recognition System for Cross-Modality Data Analysis
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Solution Overview
Problem
Current automated pattern recognition systems are limited by specific modalities and require extensive human analysis, leading to inefficiencies and errors due to the need for manual processing and adaptation to different data types, which hinders their broad applicability and effectiveness.
Innovation Solution
A data analysis system that uses a minimal number of algorithms to recognize patterns and detect objects across various modalities, including imagery, acoustic, and tactile data, without requiring adaptation to specific applications or data forms, allowing for rapid development and improvement while operating on native data resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human analysis is used to evaluate digital data, then expertise and judgment can be applied, but the process becomes expensive and error-prone due to human limitations
Solution Approach 1:
The patent replaces manual human analysis with an automated computer-based pattern recognition system that processes digital data without human intervention, eliminating human errors and costs while maintaining analysis capability through algorithmic evaluation
Solution Approach 2:
The system enables self-service analysis where the computer automatically evaluates digital data using embedded algorithms, eliminating the need for human experts to manually review each dataset while maintaining consistent evaluation standards
2Ease of operation
If data is processed and filtered for presentation before analysis, then human readability is improved, but significant information is lost from the original data
Solution Approach 1:
Instead of filtering data before analysis to improve readability, the patent inverts the approach by having the computer system directly analyze the raw digital data in its native form, then presenting only the essential findings to human users, thus preserving all original information while maintaining usability
Solution Approach 2:
The patent introduces an automated pattern recognition system as an intermediary between the raw digital data and human users, which processes the complete unfiltered dataset and translates complex findings into human-perceivable representations without requiring human users to directly handle the raw data
3Reliability
If pattern recognition systems are designed for specific modalities, then they can achieve high performance on that data type, but they cannot be easily adapted to other data types
Solution Approach 1:
The patent creates a universal pattern recognition system that can evaluate multiple types of digital data (medical images, seismic data, financial data, etc.) using the same core algorithms and processing architecture, eliminating the need for separate specialized systems for each data modality
Solution Approach 2:
The patent enables cross-modality application by allowing the system to adjust evaluation parameters and algorithms based on the specific characteristics of each data type being analyzed, maintaining high performance across different modalities through parameter optimization rather than requiring separate specialized systems
4Measurement precision
If complex algorithms are used for pattern and feature recognition, then analysis accuracy is improved, but processing time and system development delay increase
Solution Approach 1:
The patent implements continuous processing where the automated system evaluates digital data without interruption or manual intervention, eliminating the incremental delays associated with discovering, developing, and implementing new algorithms for each analysis task through sustained automated operation
Solution Approach 2:
The patent performs preliminary pattern recognition and feature extraction automatically before human review, preparing analyzed results in advance so that when human experts do review the data, the most time-consuming computational analysis has already been completed, reducing overall processing time
Data Source
AI summary
Systems and methods for aggregating and using data corresponding to physical samples in a virtual environment. The method includes receiving a first physical sample, sensing a first aspect of the first sample to generate first sample first aspect data, storing the first sample first aspect data in a datastore, sensing a second aspect of the first sample to generate first sample second aspect data, storing the first sample second aspect data in the datastore, generating first sample transformed data by running a first series of algorithms using at least one of the first sample first aspect data and the first sample second aspect data, and storing the first sample transformed data in the datastore.